All lines of business are under pressure to meet targets and deliver expected results, but none is under more pressure than Sales. Like other organizations it must use information to derive insights about progress and problems and to decide what changes to make. Today businesses collect and analyze data from more data sources in more forms than ever before. To understand it they need effective analytics, and again none need it more than Sales.

Analytics applied to sales data can deliver significant value. It can help sales organizations reach quotas, forecast more accurately and make better decisions about activities and strategy. However, selecting the right tools for analytics can be difficult. People charged with identifying those tools often don’t understand the specific needs of sales groups or the full scope of evaluation criteria required for successful deployment and use.

In the past, and often still today, many sales organizations did not invest directly in analytics, instead using desktop spreadsheets, cutting data and charts from applications and reports and pasting it into presentations. In addition organizations have tried to stretch applications designed for other purposes, such as sales force automation (SFA) and customer relationship management (CRM), to provide reports on accounts and opportunities, but these systems address only some sales activities and lack complete information about customers. They need more purpose-built tools to examine sales performance in core processes such as the sales pipeline, forecasts, configuration pricing and quoting, proposals, quotas, compensation and incentives, and coaching of personnel. Used properly analytics can provide insights on all of these. But to select tools that cover all these areas, prospective users of sales analytics must be able to understand the available options and identify the capabilities that will serve them best. Should they be simple or complex? One-time or continuously used? Historical or forward-looking? In addition the tools should be easy to use and able to make data readily available. Several of our benchmark research studies demonstrate the need for effective analytics in this critical business area of sales.

The expanding use of sales analytics stems from increased attention to performance across sales processes (particularly because of the importance of sales in revenue and financial planning to meet customer demand with products and services). In sales planning, the need for quick analysis and review of actuals vs. plan is the management capability most often important (for 73%) for organizations participating in our next-generation business planning benchmark research. Many sales initiatives have failed to live up to their potential because companies did not use the right analytics tools and approaches to plan and operate them. For example, measuring the wrong things, measuring the right things the wrong way or using only partial data to measure will have negative (and often unintended) consequences. That’s because sales decisions almost always must be made in a constrained environment, in which virtually every important decision requires trade-offs (such as price against volume). To be useful, analytics must recognize trade-offs and provide guidance to help decision-makers choose those that are aligned not only with the company’s overall objectives but also with its customer, financial and sales profitability goals. Sales analytics also can support new approaches such as sales contests and gamification that motivate employees to perform better.

Analytics also is a key tool for devising, tracking and revising measures and metrics for sales processes that enable managers to make better-informed decisions faster and more consistently. Sales operations managers and executives need advice on selecting the analytics most useful for them and choosing sales metrics and performance indicators. This applies to the various kinds of analytics that sales organizations need. For example, our research finds that analytics is a priority for sales compensation management, where it is the top technology trend in 84 percent of organizations, and for sales forecasting (in 79% of organizations).

At this level of complexity sales organizations cannot continue to make do with outmoded tools. While spreadsheets are comfortable and familiar, when used for collaborative enterprise tasks they fall short in many areas such as lacking control of calculations and introducing inconsistent or erroneous data. Our research in sales compensation management finds that those that use them universally for this purpose are not satisfied with their analytics tools more often (in more than two-thirds of organizations) than those that use more capable tools.

A new generation of technologies offers more powerful and flexible sales analytics. Big data systems for processing and storing data have evolved quickly, making it possible for sales organizations to extract insights from masses of data for practical purposes. For example, visual discovery can present data in quickly graspable forms, and simplifying the presentation of key sales indicators and metrics in dashboards can put necessary information at the fingertips of nontechnical executives and managers. Advances in mobile technology enable access to analytics from smartphones and tablets, helping users on the go decide what to do next. Making sales applications and data available through cloud computing also facilitates access to and use of sales analytics. In addition collaboration enables sales teams to communicate and coordinate operations and managers and sales reps to plan more effectively. And predictive analytics enables users to look forward to anticipate trends and plan ahead rather than merely react.

Analyzing these advances and their impacts on sales organizations now and in the near future, we have released new benchmark research on the next generation of sales analytics. It examines the use of and intentions for analytics and metrics involved in sales-related activities. It uses the Ventana Research Performance Index Model® to assess the productivity and performance of organizations by size and industry. A major aim of this research is to provide understanding of the need for and potential of advanced tools to help set a business case for investing in sales analytics. It assesses the impacts of these next-generation technologies in facilitating faster, easier and broader use of sales analytics. It follows up on previous research that shows that inconsistent execution and scattered information continue to motivate investment in about half of sales organizations and explores how advanced analytics can help remedy these issues.

Our mission in this new research is to uncover the best practices companies use in measuring the performance of sales-related activities, the challenges they face and how they intend to improve their situations in the coming years. Sales analytics is a required component for any successful use of SFA to applications and activities individually or as part of sales performance management. It examines in detail issues in collecting data from a diverse set of sources that are critical for creating and maintaining useful sales metrics and indicators. It explores how data sources are reconciled through data preparation and then analyzed to produce the information sales people require to support decisions that impact their bottom line, market share and other aspects of their strategic objectives. Increasing the accuracy, confidence and timeliness of sales analytics is critical to every activity in sales, and those without a dedicated approach designed to assist sales will find themselves at a disadvantage. Sales analytics is a key resource for sales organizations facing today’s unprecedented challenges, as I recently outlined in our Sales Research Agenda for 2015. If you are responsible for sales activities or systems and want to see where the present and the future lie, please consult our research on the next generation of sales analytics.

Data is an essential ingredient for every aspect of business, and those that use it well are likely to gain advantages over competitors that do not. Our benchmark research on information optimization reveals a variety of drivers for deploying information, most commonly analytics, information access, decision-making, process improvements and customer experience and satisfaction. To accomplish any of these purposes requires that data be prepared through a sequence of steps: accessing, searching, aggregating, enriching, transforming and cleaning data from different sources to cre­ate a single uniform data set. To prepare data properly, businesses need flex­ible tools that enable them to en­rich the context of data drawn from multiple sources, collaborate on its preparation to serve business needs and govern the process of preparation to ensure security and consistency. Users of these tools range from analysts to operations professionals in the lines of business.

Data preparation efforts often encounter challenges created by the use of tools not designed for these tasks. Many of today’s analytics and business intelligence products do not provide enough flexibility, and data management tools for data integration are too complicated for analysts who need to interact ad hoc with data. Depending on IT staff to fill ad hoc requests takes far too long for the rapid pace of today’s business. Even worse, many organizations use spreadsheets because they are familiar and easy to work with. However, when it comes to data preparation, spreadsheets are awkward and time-consuming and require expertise to code them to perform these tasks. They also incur risks of errors in data and inconsistencies among disparate versions stored on individual desktops.

In effect inadequate tools waste analysts’ time, which is a scarce re­source in many organizations, and can squander market opportunities through delays in preparation and unreliable data quality. Our information optimization research shows that most analysts spend the majority of their time not in actual analysis but in readying the data for analysis. More than 45 percent of their time goes to preparing data for an­al­y­sis or reviewing the quality and consistency of data.

Businesses need technology tools capable of handling data preparation tasks quick­ly and dependably so users can be sure of data quality and concen­trate on the value-adding as­pects of their jobs. More than a dozen such tools designed for these tasks are on the market. The best among them are easy for analysts to use, which our research shows is critical: More than half (58%) of participants said that usability is a very important evaluation criterion, more than any other, in software for optimizing information. These tools also deal with the large numbers and types of sources organizations have accumulated: 92 percent of those in our research have 16 to 20 data sources, and 80 percent have more than 20 sources. Complicating the issue further, these sources are not all inside the enterprise; they also are found on the Internet and in cloud-based environments where data may be in applications or in big data stores.

Organizations can’t make business use of their data until it is ready, so simplifying and enhancing the data preparation process can make it possible for analysts to begin analysis sooner and thus be more productive. Our analysis of time related to data preparation finds that when this is done right, significant amounts of time could be shifted to tasks that contribute to achieving business goals. We conclude that, assuming analysts spend 20 hours a week working on analytics, most are spending six hours on preparing data, another six hours on reviewing data for quality and consistency issues, three more hours on assembling information, another two hours waiting for data from IT and one hour presenting information for review; this leaves only two hours for performing the analysis itself.

Dedicated data preparation tools provide support for key tasks in areas that our research and experience finds that are done manually by about one-third of organizations. These data tasks include search, aggregation, reduction, lineage tracking, metrics definition and collaboration. If an organization is able to reduce the 14 hours previously mentioned in data-related tasks (that including preparing data, reviewing data and waiting for data from IT) by one-third, it will have an extra four hours a week for analysis – that’s 10 percent of a 40-hour work week. Multiply this time by the number of individual analysts and it becomes significant. Using the proper tools can enable such a reallocation of time to use the professional expertise of these employees.

This savings can apply in any line of business. For example,our research into next-generation finance analytics shows that more than two-thirds (68%) of finance organizations spend most of their analytics time on data-related tasks. Further analysis shows that only 36 percent of finance organizations that spend the most time on data-related tasks can produce metrics within a week, compared to more than half (56%) of those that spend more time on analytic tasks. This difference is important to finance organizations seeking to take a more active role in corporate decision-making.

Another example is found in big data. The flood of business data has created even more challenges as the types of sources have expanded beyond just the RDBMS and data appliances; Hadoop, in-memory and NoSQL big data sources exist in at least 25 percent of organizations, according to our big data integration research. Our projections of growth based on what companies are planning indicates that Hadoop, in-memory and NoSQL sources will increase significantly. Each of these types must draw from systems from various providers, which have specific interfaces to access data let alone load it. Our research in big data finds similar results regarding data preparation: The tasks that consume the most time are reviewing data for quality and consistency (52%) and preparing data (46%). Without automating data preparation for accessing and streamlining the loading of data, big data can be an insurmountable task for companies seeking efficiency in their deployments.

A third example is in the critical area of customer analytics. Customer data is used across many departments but especially marketing, sales and customer service. Our research again finds similar issues regarding time lost to data preparation tasks. In our next-generation customer analytics benchmark research preparing data is the most time-consuming task (in 47% of organizations), followed closely by reviewing data (43%). The research also finds that data not being readily available is the most common point of dissatisfaction with customer analytics (in 63% of organizations). Our research finds other examples, too, in human resources, sales, manufacturing and the supply chain.

The good news is that these busi­ness-focused data preparation tools have usability in the form of spreadsheet-like interfaces and include analytic workflows that simplify and enhance data preparation. In searching for and profiling of data and examining fields based on analytics, use of color can help highlight patterns in the data. Capabilities for addressing duplicate and incorrect data about, for example, companies, addresses, products and locations are built in for simplicity of access and use. In addition data preparation is entering a new stage in which ma­chine learning and pat­tern recog­ni­tion, along with predictive analytics techniques, can help guide individuals to issues and focus their efforts on looking forward. Tools also are advancing in collaboration, helping teams of analysts work together to save time and take advantage of colleagues’ expertise and knowledge of the data, along with interfacing to IT and data management professionals. In our information optimization research collaboration is a critical technology innovation, according to more than half (51%) of organizations. They desire several collaborative capabilities ranging from discussion forms to knowledge sharing to requests on activity streams.

This data preparation technology provides support for ad hoc and other agile approaches to working with data that maps to how business actually operate. Taking a dedicated approach can help simplify and speed data preparation and add value by enabling users to perform analysis sooner and allocate more time to it. If you have not taken a look at how data preparation can improve analytics and operational processes, I recommend that you start now. Organizations are saving time and becoming more effective by focusing more on business value-adding tasks.